{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#项目介绍\" data-toc-modified-id=\"项目介绍-1\">项目介绍</a></span></li><li><span><a href=\"#哪些类别比较畅销?\" data-toc-modified-id=\"哪些类别比较畅销?-2\">哪些类别比较畅销?</a></span></li><li><span><a href=\"#哪些商品比较畅销?\" data-toc-modified-id=\"哪些商品比较畅销?-3\">哪些商品比较畅销?</a></span></li><li><span><a href=\"#不同门店的销售额占比\" data-toc-modified-id=\"不同门店的销售额占比-4\">不同门店的销售额占比</a></span></li><li><span><a href=\"#哪个时间段是超市的客流高封期?\" data-toc-modified-id=\"哪个时间段是超市的客流高封期?-5\">哪个时间段是超市的客流高封期?</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 项目介绍\n",
    "近些年来，国内大型连锁超市如雨后春笋般迸发，对于各个超市来说，竞争压力不可谓\n",
    "不大，为了拓展、保留客户，各种促销手段应运而生。\n",
    "\n",
    "以下为国内某连锁超市的成交统计数据，针对于该数据，挖掘其中价值，为该超市的促销手段提供技术支持。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3478, 7)\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>商品ID</th>\n",
       "      <th>类别ID</th>\n",
       "      <th>门店编号</th>\n",
       "      <th>单价</th>\n",
       "      <th>销量</th>\n",
       "      <th>成交时间</th>\n",
       "      <th>订单ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30006206</td>\n",
       "      <td>915000003</td>\n",
       "      <td>CDNL</td>\n",
       "      <td>25.23</td>\n",
       "      <td>0.328</td>\n",
       "      <td>2017-01-03 09:56:00</td>\n",
       "      <td>20170103CDLG000210052759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30163281</td>\n",
       "      <td>914010000</td>\n",
       "      <td>CDNL</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2017-01-03 09:56:00</td>\n",
       "      <td>20170103CDLG000210052759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30200518</td>\n",
       "      <td>922000000</td>\n",
       "      <td>CDNL</td>\n",
       "      <td>19.62</td>\n",
       "      <td>0.230</td>\n",
       "      <td>2017-01-03 09:56:00</td>\n",
       "      <td>20170103CDLG000210052759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29989105</td>\n",
       "      <td>922000000</td>\n",
       "      <td>CDNL</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.044</td>\n",
       "      <td>2017-01-03 09:56:00</td>\n",
       "      <td>20170103CDLG000210052759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30179558</td>\n",
       "      <td>915000100</td>\n",
       "      <td>CDNL</td>\n",
       "      <td>47.41</td>\n",
       "      <td>0.226</td>\n",
       "      <td>2017-01-03 09:56:00</td>\n",
       "      <td>20170103CDLG000210052759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       商品ID       类别ID  门店编号     单价     销量                成交时间  \\\n",
       "0  30006206  915000003  CDNL  25.23  0.328 2017-01-03 09:56:00   \n",
       "1  30163281  914010000  CDNL   2.00  2.000 2017-01-03 09:56:00   \n",
       "2  30200518  922000000  CDNL  19.62  0.230 2017-01-03 09:56:00   \n",
       "3  29989105  922000000  CDNL   2.80  2.044 2017-01-03 09:56:00   \n",
       "4  30179558  915000100  CDNL  47.41  0.226 2017-01-03 09:56:00   \n",
       "\n",
       "                       订单ID  \n",
       "0  20170103CDLG000210052759  \n",
       "1  20170103CDLG000210052759  \n",
       "2  20170103CDLG000210052759  \n",
       "3  20170103CDLG000210052759  \n",
       "4  20170103CDLG000210052759  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "# 导入数据源，parse_dates：将时间字符串转为日期时间格式\n",
    "data=pd.read_csv(\"order-14.3.csv\",parse_dates=[\"成交时间\"],encoding='gbk')\n",
    "print(data.shape)\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 哪些类别比较畅销?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>类别ID</th>\n",
       "      <th>销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>922000003</td>\n",
       "      <td>425.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>922000002</td>\n",
       "      <td>206.424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>923000006</td>\n",
       "      <td>190.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>915030104</td>\n",
       "      <td>175.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>922000001</td>\n",
       "      <td>121.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>960000000</td>\n",
       "      <td>121.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>920090000</td>\n",
       "      <td>111.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>923000002</td>\n",
       "      <td>91.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>922000000</td>\n",
       "      <td>86.395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>923000000</td>\n",
       "      <td>85.845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          类别ID       销量\n",
       "240  922000003  425.328\n",
       "239  922000002  206.424\n",
       "251  923000006  190.294\n",
       "216  915030104  175.059\n",
       "238  922000001  121.355\n",
       "367  960000000  121.000\n",
       "234  920090000  111.565\n",
       "249  923000002   91.847\n",
       "237  922000000   86.395\n",
       "247  923000000   85.845"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ascending=False 降序\n",
    "data.groupby(\"类别ID\")[\"销量\"].sum().reset_index().sort_values(by=\"销量\",ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 哪些商品比较畅销?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>商品ID</th>\n",
       "      <th>销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>29989059</td>\n",
       "      <td>391.549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>29989072</td>\n",
       "      <td>102.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>30022232</td>\n",
       "      <td>101.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>30031960</td>\n",
       "      <td>99.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>29989157</td>\n",
       "      <td>72.453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>30023041</td>\n",
       "      <td>64.416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>30026255</td>\n",
       "      <td>62.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29989058</td>\n",
       "      <td>56.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>30027007</td>\n",
       "      <td>48.757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>30171264</td>\n",
       "      <td>45.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         商品ID       销量\n",
       "8    29989059  391.549\n",
       "18   29989072  102.876\n",
       "469  30022232  101.000\n",
       "523  30031960   99.998\n",
       "57   29989157   72.453\n",
       "476  30023041   64.416\n",
       "505  30026255   62.375\n",
       "7    29989058   56.052\n",
       "510  30027007   48.757\n",
       "903  30171264   45.000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(data,index=\"商品ID\",values=\"销量\",aggfunc=\"sum\").reset_index().sort_values(by=\"销量\",ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同门店的销售额占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "门店编号\n",
      "CDLG    10908.82612\n",
      "CDNL     8059.47867\n",
      "CDXL     9981.76166\n",
      "Name: 销售额, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售额占比</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>门店编号</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CDLG</th>\n",
       "      <td>0.376815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDNL</th>\n",
       "      <td>0.278392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDXL</th>\n",
       "      <td>0.344792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         销售额占比\n",
       "门店编号          \n",
       "CDLG  0.376815\n",
       "CDNL  0.278392\n",
       "CDXL  0.344792"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"销售额\"]=data[\"销量\"]*data[\"单价\"]\n",
    "# 不同门店销售\n",
    "print(data.groupby(\"门店编号\")[\"销售额\"].sum())\n",
    "# 不同门店销售额占比\n",
    "dfbb = data.groupby(\"门店编号\")[[\"销售额\"]].sum()/data[\"销售额\"].sum()\n",
    "dfbb.rename(columns={'销售额':'销售额占比'},inplace=True)\n",
    "dfbb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='销售额'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GhZuOAtQIhRL4i9Jyhx5btEVv/pBqOgoAHxToZ9Wd57XT2IGJslgspuMA1UKhBP5kX1ahxr+3TuvTsk1HAeDjBiQ30dOXdlOziGDTUYBTolACv/lq82Hd/uF65RSxSTkA9xAR7K+H/95ZF3VrbjoKcFIUSvi8crtDT32+Ta9+v0v8bQDgjv7Zq6UevbizAv1spqMAVaJQwqcdyinWze+t1Rr2lgTg5rrHN9YrV/dSHCfswA1RKOGzvv/1iP5vzs/KKCg1HQUAqiU2PFCvXN1LPRIiTUcBKqFQwuc4HE4999V2TVu6Qw7+9APwMAF+Vj12cRf9s1dL01GAChRK+JQjeSW6bc46/bAzw3QUAHDJdQNa674LOspmZWshmEehhM9YuStDt76/Tul5JaajAECdGJgcrWlX9FDjkADTUeDjKJTwCW/9kKqHFm6WnTluAF6mVZMQzbymN6frwCgKJbya0+nUlMVb9ep3u0xHAYB6Expg0zMju+u8Tk1NR4GPolDCa5WU23XHh79owfoDpqMAQL2zWKT/G9pWtw5N5shGNDgKJbxSTlGZ/vX2Gq3anWk6CgA0qGGdm2rqZd0UEuBnOgp8CIUSXmd/dpFGv/Gjfk3PNx0FAIxo3zRcM6/prfioENNR4CMolPAqmw/k6ro3f9ThXFZyA/BtUaEBmn5lT/VLamI6CnwAhRJe4/tfj2jcu2uVX1JuOgoAuIVAP6tevrKnhnaIMx0FXo5CCa8w76d9+s/8X1Rm548zAPyZv82iqZd110XdmpuOAi9GoYTHe+HrX/XMl9tNxwAAt2W1SI9e3EWj+iaYjgIvRaGExyq3O3Tfxxs1Z3Wa6SgA4BHuu6CDrj8jyXQMeCEKJTxSYWm5xs9eq6XbjpiOAgAe5dahKfp/57Q1HQNehkIJj3Mkr0Rj3lytDftzTEcBAI80dmCi7r+wo+kY8CIUSniUfVmFumLmKu3NLDQdBQA82uV94vXYxV1ktXKqDlxHoYTHSMss1KiZK7Uvq8h0FADwChd2baZnR3aXv81qOgo8HIUSHiEts1CXv7pS+7MpkwBQl85qH6uXr+ypIH+b6SjwYBRKuD3KJADUr/5JUXrt2j4KC+T8b9QOhRJujTIJAA2jW3xjvXVdHzUOCTAdBR6IQgm3RZkEgIbVvmm43h7bV7HhQaajwMNQKOGWsrIydeErP1MmAaCBJcWEat5NpysqlJFKVB/LuuB+cvYr8q3BeizmC9NJAMDn7DpSoOtm/aiCknLTUeBBKJRwL/np0tsXSdl7NThtut5P+dZ0IgDwOev35ejGd35SabnDdBR4CAol3EdhpvT236SMHRU3nZb2qj5M+cpgKADwTf/bcVQTPvhZDgdXxuHUKJRwD8U50jt/l9I3H3dXn7Q39HHKkobPBAA+7rNfDmrygk2mY8ADUChhXmmhNPtS6eD6Ez6ke9rbWpjyWQOGAgBI0tsr9ui5r7abjgE3R6GEWQ67NG+MlLbqlA/tnDZbn6d8IouF6RcAaEjPffWr3l25x3QMuDEKJcxafJe0fXG1H94uba6+TJ5PqQSABjbpk41atOGg6RhwUxRKmPO/56TVr9X4aclpH+mbNh/KZmH1IQA0FIdT+r85P2v5jqOmo8ANsbE5zNj4kTRvrKTa//FLa3mhzto1SmUOS93lAgCcVFign96/ob+6tIwwHQVuhBFKNLw9P0j/HSdXyqQkxe9bqGWJ7yjQykglADSU/JJyjZ71o3YfLTAdBW6EQomGdWS79P4oyV5SJy/XfP8SLWv9loJt9jp5PQDAqWUUlOrq11fpcG6x6ShwExRKNJz8dGn2P6Xi7Dp92aYHvtR3rd5QqB+lEgAayr6sIl3z+o/KKSwzHQVugEKJhlFaKL13mZRdP9tOxBxYqu/iZyrcj7NnAaChbDucp7FvrVZJOf+g93UUStQ/h12ad510YF29vk2Tg9/p+5YzFOlPqQSAhrJmT5Ymf8ppOr6OQon6t+hOaXvDHJ3Y+NAPWtb8JTUJYAoGABrK+z+mac6Pe03HgEEUStSvNbOkNa836Fs2OrxK3zZ9UbGBlEoAaCiTPt2k9WnZpmPAEAol6s/+n6TFdxt56/D0Nfom9jk1Cyo18v4A4GtKyx369+y1yizg564volCifhRkSHOvqbPtgWoj7Mg6fR39jFoGmcsAAL5kf3aRbnl/rewOzkzxNRRK1L3fF+Hk7jOdRCFHf9GXTZ5W62D2SgOAhrB8R4ae+nyb6RhoYBRK1L2vH5J2LzOdokJwxiYtiXxSKaFFpqMAgE+YsWynlmw8aDoGGhCFEnVrywJp+XOmUxwnKHOrPmv0uNqHFZqOAgA+4Y4Pf9GO9DzTMdBAKJSoO0d/lT7+t+kUJxSQ9as+DXtMXcI5fxYA6lt+SblufOcn5ZewN7AvoFCibpTkS3OvkkpyTSc5qYDsXZof8qh6RuSbjgIAXm/nkQLd+eF60zHQACiUqBuf3iwd2Wo6RbX456Tqg8CH1bexe5dfAPAGizce0vRvd5qOgXpGoYTrfpgmbfqv6RQ14pebpvf8H9aAyBzTUQDA6z39xTYt33HUdAzUIwolXLN3pfTVA6ZT1Ipf3n69bX1Qg5tkmY4CAF7N7nDqlvfXaX82u214Kwolaq8kT5r/L8nhuRdc2woO6Q09qKFNMk1HAQCvlllQqnHv/qSScrvpKKgHFErU3uJ7pOw9plO4zFaQrpmOyRoWw3QMANSnX/bl6InFbHrujSiUqJ0tC6Sf3zWdos5Yi47qpfLJGhF7xHQUAPBqs37YrR928g94b0OhRM3lHZYW3GY6RZ2zFmXq+dIH9I+4w6ajAIDXcjqlOz/8RbnFZaajoA5RKFFzn4yXCjNMp6gX1uJsPV08SZc348gwAKgv+7OLNPnTTaZjoA5RKFEzP86UdnxpOkW9spTkaUrBA7qm+QHTUQDAa81fu5/zvr0IhRLVd/RX6ctJplM0CEtpvh7Me0BjW6SZjgIAXuve/25Uel6x6RioAxRKVI+9/NgWQWWFppM0GEtZge7Lmaxx8Z6/kh0A3FFmQanu+WiD6RioAxRKVM+yJ6QDa02naHCW8iLdlTlZtyXsMh0FALzSN1vTNefHvaZjwEUUSpxa2o/S91NNpzDGYi/R/2U8pNsTOIsWAOrDo59t0cEcTtHxZBRKnFxZkfTfGyWnb59sYLGX6uajD+meVttNRwEAr5NXUq6J/91oOgZcQKHEyX33lJTJdK8kWRxluvHII5qUuMV0FADwOt9sTdd/1+0zHQO1RKHEiaVvlZa/YDqFW7E4ynXdocf0SBL7pwFAXXtwwWYdySsxHQO1QKFE1ZxOaeEEycFJBn9lcdp15cEpeiLpF9NRAMCrZBeW6YFPmfr2RBRKVG3du9LeH0yncFsWp0OXHXhCz7RZZzoKAHiVRRsOafEGNjz3NBRKHK8gw2c2MHeFRU5dvP9pvZS82nQUAPAq93+ySdmFpaZjoAYolDjeF/dJRZmmU3gEi5y6YN+zejV5pekoAOA1juaX6PHFW03HQA1QKFHZ7u+l9e+ZTuFxzt33gmalLDcdAwC8xgdr0rT5QK7pGKgmCiX+UF4qffb/TKfwWGemvaTZKctMxwAAr+BwSg8tZEcNT0GhxB+WPy8dZeNuVwxIe0UfpHxtOgYAeIWVuzL1+aZDpmOgGiiUOCZjp/T906ZTeIW+aa9rfsoXpmMAgFeYsmiLSssdpmPgFCiUOGbRHVJ5sekUXqNn2pta0HaR6RgA4PFSMwr15g+7TcfAKVAoIW1bLO38xnQKr9Nl77tanPKpLBan6SgA4NFe/GaHMvI5QcedUSh9ncMufTXZdAqv1SFtjr5I/phSCQAuyCsu1zNfco2/O6NQ+rp170hH2OurPqWkfahvkj+UzcI1QABQW3NWp2nboTzTMXACFEpfVlooLZ1iOoVPSEz7WEvbzJW/lZFKAKgNu8OpRz7bbDoGToBC6ctWTJPy2Y6hoSTsW6ClSbMVaGWkEgBq4/tfj+rrLYdNx0AVKJS+Kv+ItPwF0yl8Tst9i7Qs8W0F2+ymowCAR3p00RaV2fmHubuhUPqqZY9LpVyLYkLT/V9oWas3FWrjByIA1NSuIwV6Z8Ue0zHwFxRKX3R0h/TTm6ZT+LTYA1/ru4SZCvcrNx0FADzO81//quzCUtMx8CcUSl/09WTJQZExrcnBZfqu5SuK8Od/CwCoiZyiMj3LNkJuhULpa9J+lLYsMJ0Cv4k8tFzfNX9ZTQLKTEcBAI8ye9Ve7Ujn0i13QaH0NV/cbzoB/iLi8EotbTpNMZRKAKi2codTU79glNJdUCh9ydbPpLSVplOgCo3SV2tp3PNqGsg1QQBQXZ9vOqQd6fmmY0AUSt+y7AnTCXASYUfW6puYZ9UiiPNqAaA6HE5pxrKdpmNAFErf8etX0sH1plPgFEKOrtdXTaaqdXCx6SgA4BE++Xm/9mcXmY7h8yiUvuL7p00nQDUFZ2zUksin1CaEH5AAcCpldqdeZZTSOAqlL0hdLu1dYToFaiAoc4sWRTyh9mGFpqMAgNubuyZNR/O5XMgkCqUv+H6q6QSohcCs7fo0bIo6hReYjgIAbq24zKE3/rfbdAyfRqH0dgfWSTu/Np0CtRSQvVMfhzym7o1YxQgAJ/POyj3KLWb7NVMolN6O0UmP55+zW/OCHlbvCDbwBYATySsu54xvgyiU3uzINmnLQtMpUAf8ctM0J+AhnRaZYzoKALitWct3q7jMbjqGT6JQerPvn5HkNJ0CdcQvb7/etT2kQVHZpqMAgFs6ml+qOT/uNR3DJ1EovVVWqrRxnukUqGO2/IOaZXlQZzXJMh0FANzSzO93q8zuMB3D51AovdXy5yVHuekUqAe2gsN6zfmAzo3ONB0FANzO/uwifbxuv+kYPodC6Y3yDknrZptOgXpkLTyqGfYHdGHMUdNRAMDtzFi2Uw4Hl3w1JAqlN1ozS7Kzwau3sxZl6IWySbokLt10FABwKzuPFOjzTYdMx/ApFEpvYy+X1r5lOgUaiLU4W1OLJ2lkM35wAsCfvfwtxzE2JAqlt9n2mZR30HQKNCBLSa4eL5ikK5sdMB0FANzGhv05+t+vXBbUUCiU3mb1a6YTwABLab4eyX9Ao5vvMx0FANzG+2wh1GAolN7kyHZp93emU8AQS1mBHsibrBtb8gMUACTpy82HlVlQajqGT6BQepM1b5hOAMMsZYW6J3uybo5PNR0FAIwrtTs0fy0zNw2BQuktSgul9e+ZTgE3YCkv1u2ZD2pCwi7TUQDAuA/WpJmO4BMolN5iw4dSMec84xiLvUS3Hn1Qd7f61XQUADBq++F8rdvL6WL1jULpLda8bjoB3IzFUaabjjys+xO3mo4CAEYxSln/KJTeYN8a6eB60ynghiyOco059KgeTtxkOgoAGLNg/UEVlnIccX2iUHoDtgrCSVicdl11aIqmJG0wHQUAjMgvKdfCX9ijuT5RKD1dYaa06b+mU8DNWZwOXX7gcT3TZp3pKABgxAermfauTxRKT7d+jlRebDoFPIBFTl28/2m9mPyT6SgA0ODW7MnSjvR80zG8FoXS0/0yx3QCeBCLnBqxb6peSV5lOgoANLgPWZxTbyiUnuzINhbjoFbO2/e83kj5wXQMAGhQH63drzK7w3QMr0Sh9GS/zDWdAB7srLRpeieFozoB+I6j+SX6eku66RheiULpqZzOY5uZAy44I22G5qR8YzoGADQY9qSsHxRKT7V3hZS913QKeIH+aa9pXtuvTMcAgAaxbPsRHc5lMWtdo1B6ql8+MJ0AXqT33jf0SdvFpmMAQL2zO5ya99M+0zG8DoXSE9nLpc2fmE4BL9Nt7ztalLLAdAwAqHes9q57FEpPtOtbqSjTdAp4oY5p7+vzlI9lsThNRwGAepOaUahth/JMx/AqFEpPtGm+6QTwYu3SPtDXyR/JZmFrDQDe66sth01H8CoUSk9TXiptXWg6BbxcUtp8fdPmA0olAK/15WYKZV2iUHqanV9LxTmmU8AHtNr3qZa1eV/+Vqa/AXif9fuydSSvxHQMr0Gh9DQbme5Gw2m57zN9l/i2Aq2MVALwLk6n9DXT3nWGQulJ7OXS9s9Np4CPabb/c33X+k2F2iiVALwL11HWHQqlJ0lbKZUw3Y2GF3fgKy1r9ZpC/eymowBAnfnfjqMqLuPnWl2gUHqSX78wnQA+LPrAt/o+/lWF+5WbjgIAdaK4zKHvfz1qOoZXoFB6kl+/NJ0APi7q4Pf6vuUMRfpTKgF4h69Y7V0nKJSeImeflL7ZdApAjQ/9oGXNpykmoMx0FABw2ddb0+V0spuFqyiUnoLpbriRRod/1NKmL6hpYKnpKADgkqP5JVqXlm06hsejUHoKprvhZsLSf9LXsc+pRRD7uAHwbEx7u45C6QnKS6Rdy0ynAI4TeuRnfRn9jBKCi01HAYBaY/sg11EoPUHq/6SyAtMpgCqFHN2gL6KeVlIIpRKAZ9p+OF97MwpNx/BoFEpPwHQ33FxQxmYtjnhCbUOLTEcBgFr5klFKl1AoPQELcuABArO2aWH4FHUI41/5ADwP11G6hkLp7jJ2Spk7TacAqiUge4c+DX1MXRvlm44CADWyOjVTOUVsh1ZbFEp3t+Mr0wmAGvHP2aWPgh5Vz4g801EAoNrKHU79uDvTdAyPRaF0d6zuhgfyz92jDwIeVt/GuaajAEC1rd2bZTqCx6JQuru0VaYTALXil7dP7/s9pAGROaajAEC1/LSHQllbFEp3dnSHVMih9fBctvwDetv6oIZE8UMagPv7ZV+2yu0O0zE8EoXSnaWtNJ0AcJmt4JDe0GSdE821SQDcW3GZQ5sOcKlObVAo3dneFaYTAHXCWnhEr9gf0PAYRtwBuDemvWuHQunO9nL9JLyHtShD08oe0N/i0k1HAYAT+omFObVCoXRXBRlSxq+mUwB1ylqcpeeKH9A/m7KBMAD3tJYRylqhULorVnfDS1lKcvRU0SRd0eyg6SgAcJyDOcU6mMMxsjVFoXRXXD8JL2YpydOjBZN0bfP9pqMAwHG4jrLmKJTuihFKeDlLaYEm5z2g61ummY4CAJVQKGuOQumOykukAz+bTgHUO0tZoSZmT9a4+FTTUQCgAtdR1hyF0h0dWCfZS0ynABqEpbxId2U+qP9L2GU6CgBIkjYfzFVxmd10DI9CoXRHXD8JH2Oxl+i2jId0R6sdpqMAgMrsTq1PyzYdw6NQKN3RgXWmEwANzmIv1fgjD2ti622mowAA+1HWEIXSHR3ebDoBYITFUabrDz+iyYlbTEcB4OO4jrJmKJTupqxYyuRaMvgui9Ouaw89pseSNpiOAsCHrdubbTqCR6FQupuj2yQnFwLDt1mcdo06+ISeTlpvOgoAH5VRUKo9GQWmY3gMCqW7YbobkCRZnA7948CTer7NWtNRAPioXw/nm47gMSiU7iZ9k+kEgNuwyKm/7X9a05N/NB0FgA/afZQRyuqiULqbdBYjAH81bN9zei2F7bQANKxdRxmhrC4Kpbthyhuo0tlpL+qtlO9NxwDgQ3YdYYSyuiiU7qQoW8o7YDoF4LYGp03Xeynfmo4BwEcw5V19FEp3ks7oJHAqp6e9qg9SvjYdA4APSM8rUX5JuekYHoFC6U4OsyAHqI6+aa/rv20/Nx0DgA/YzbR3tVAo3QkLcoBq67H3LS1M+cx0DABejoU51UOhdCdMeQM10jlttj5P+UQWi9N0FABeiusoq4dC6U6ObDOdAPA47dLm6svk+ZRKAPWCQlk9FEp3UVogFWWaTgF4pOS0j/RNmw9lszhMRwHgZdg6qHoolO4iO810AsCjJe77WN+2mSN/KyOVAOpOKiOU1UKhdBc5+0wnADxe/L6FWpb4jgKtjFQCqBt5JeVKzys2HcPtUSjdRQ4jlEBdaL5/iZYlvqVgm910FABegq2DTo1C6S4olECdabr/S33XapZCbYxUAnDdLqa9T4lC6S6Y8gbqVMyBb/RdwkyF+3HKBQDXsNL71KpVKIuKivTpp58qPT291m+0bRtb4pwUhRKoc00OLtN3LV9RhD+lEkDtsdL71Pyq86CcnBxdfPHFWrRokc477zxJ0tNPPy2r1arAwEBZLJZKj7fb7SouLtYll1yiNm3a6Mknn9TEiRP1zDPP6JZbbqn7T+ENWOUN1IvIQ8v1XXO7zjo4Thml/qbjAPBAqRkUylOpVqEMCgqS0+lUcHBwxW0PPvigSkpKZLPZqnyOxWJR165dtWTJEt1zzz1q1KiRYmNj6ya1t3E4pLwDplMAXivi8Ep927RcQw/fovQSSiWAmsnILzEdwe1Va8r79xFIf//KP4hvuukmFRUVVfmrsLBQeXl5uuWWW9SsWTOtWLFCI0eOrPtP4A3yDkoOpuSA+hSevkbfxD6nZkGlpqMA8DA5RWVyONjj9mSqNUL5uxtuuEHDhg3TmWeeKafzj/+wI0eOVExMjGJiYhQXF6eOHTuqT58+GjZsmIYNG6aHHnpIHTp0qPPwXoMV3kCDCDuyTl9HP6Nzj07QvuJA03EAeAiHU8otLlPjkADTUdxWjQplWVmZZs6cqalTp1aMWtrtds2fP192e+U930JDQ/WPf/xD06ZNU2JiYt0l9kYsyAEaTMjRX/Rlk6c1LPN2pRYFmY4DwENkFVIoT6ZG2wbNmjVL2dnZ2rFjh4KCjv0gttlsKisrk8PhUEFBgfbv36/vvvtOd9xxh7799lt16tRJzzzzTL2E9xoUSqBBBWds0pLIJ5USWmQ6CgAPkVXI5TInU60Ryt9HH0tKjl2UmpSUJKvVquXLl+uuu+464fNmz56tBQsW6I477tCePXv0/PPP10FkL1SYYToB4HOCMrfqs8jHdZHlbm3NDzEdB4Cby6ZQnlS1CmVxcXGl36VjI5Pbtm3Tnj17qtw2qKioSF26dNHjjz+u8PBwTZo0SQkJCbr99tvrML6XKM4xnQDwSQFZv+rTxo/pH5b/aENeqOk4ANxYVkGZ6QhuzeL88+qaEygpKdHKlStVWFioFi1aqHHjxkpISKjysdu2bVO7du2Ou/2ss87S8uXLtX37drVq1cr15N7kg2ukzZ+YTgH4rLKI1hpZfK/W5oSZjgLATd13QQddf0aS6Rhuq1rXUAYGBmrw4MG64IIL1KNHD02bNk2StHfv3kqPy8jI0JAhQzRq1KjjXuOpp57ShAkTKJNVYYQSMMo/J1UfBD6s3hF5pqMAcFPZhYxQnkyNVnlL0ptvvqkBAwZIkpKTk3XjjTdqypQpCgsL04QJE3T48GG1adNGvXr1UmBgoAICAuRwOFRaWqrS0lI9/vjjdf4hPB6FEjDOLzdNc8If0tWR92lFVoTpOADcDItyTq5Gq7wlqV+/fkpKOjbkW15ertdee02dO3fW1KlTNXv2bF166aW6+uqrtW7dOkVERCgoKEjLly9XYGCg4uLi6vwDeIWibNMJAEjyy9uvd60PanCTLNNRALgZCuXJVatQ3nvvvTrjjDOOW3wjSYsWLVKHDh101113KTg4WM8995ykY6frLF68WAsXLpTT6dSsWbO0ePHiOg3vNRihBNyGreCQ3tCDGtok03QUAG6ERTknV61COXv2bK1atari+zFjxmj16tWyWCxq0aKFLr/8cjmdTpWUlGjDhg2VnltVCcVflOSaTgDgT2wF6ZrpmKxhMUdNRwHgJhihPLlqFcp33nlHhw8fliQdOHBA77zzjgYNGiRJKi0t1cSJEzVgwAANHjxYV1xxhVJTUyVJWVlZysjIkMViUXZ2tjIz+Rf/cUryOMcbcEPWoqN6qXyyRsQeMR0FgBtgUc7JVWvboN9ZrVZt3bpVTqdTt9xyi7766iuNGDFC5557roYMGaKWLVuqR48eKikp0cGDBytGJ39/C4vFctwRjT4vZ5/0bCfTKQCcgCOose4MfEAfHeYacMCXBfpZte2RYaZjuK0ar/KePHmyIiMjNWfOHM2ePVu333670tLS9Pe//10RERF64YUXdNFFF2nQoEG65ZZbJB0rlOXl5SosLKzzD+DxuH4ScGvW4mw97Zwk/2aTNedgM9NxABhSUu5QUaldwQE201HcUo1GKJs0aaLQ0FAVFxdr7dq1atmypRYvXqyLL75Yr7zyiq699lpJ0mmnnaaysjKtWbOm3oJ7jdTl0pvDTacAcArOgDBNCntA7xxoYToKAEN+uOcsNW8cbDqGW6pRoTyRzZs3q2PHjhXfb9y4UfHx8YqIYC+3U9q6SJpz/EbwANyP0z9UDzeapDf2x5uOAsCAz24dqE7N6TZVqfE+lFX5c5mUpM6dO1Mmq6uMywAAT2EpK9D9OZM1Ln6P6SgADCgpd5iO4LbqpFD+rqysTHv37lUdDHr6Did/OAFPYikv0l1ZD+qWhN2mowBoYHYH/eZEalQoN2/erAsvvPCE9//8889KTEys2GII1UChBDyOpbxY/y/jQd2esNN0FAANiEJ5YjVa5V1YWKilS5eqqKhIY8eO1e233664uDiNGTNGH330kYKCgiRJwcHBWrhwoUJCQuTn51exyruoqOikhdQnUSgBj2Sxl+rmow/Jv9VEPb6nrek4ABqAg0J5QjUqlMHBwQoMDJTT6dScOXN02223qaCgQF9//bWsVqv8/PwqHnfRRRdVeUoO+1D+BYUS8FgWR5luPPKIAhL/o4d2dzAdB0A9s3NJ3wnVqFD6+fnJz8+vYiQyPDy84r6QkBBZrcdm0AMCAiRJjz76qJo1ayan06kxY8ZoypQpdZXbe1AoAY9mcZTrukOPKSDpHt23i0MKAG9WzgjlCdV4Y3NJFcXx9+lsqeozuy+55BK1bXtsKmjMmDG6+OKLa5vTezkYsQU8ncVp15UHp8g/6S7dvaur6TgA6glT3idWp6u8UQuMUAJeweJ06LIDT+iZNutMRwFQT1iUc2LVKpQOh0PTpk3T/v375XA4tHfvXknSgQMHtH//fklSWlpaxdeoAQol4DUscuri/U/rpeTVpqMAqAcUyhOr1pT3BRdcoC+++ELjx49XZmamEhMTJUlDhw6VdOys7tatW9dbSK/GBb6AV7HIqfzGmxTe4SPTUQDUMb9Gz0hqZjqGW6rWCOXIkSP1/vvva/z48QoODtakSZMkSf/+9781btw4WSwWTZo0SePGjavXsF7JyTWUgDdJa9JKU0s4SQfwRlYLVwqeSI3O8t62bZvOOOMMpaeny2q1auvWrXI6nerYsaPsdru2bt2qTp06yW63V9z/+6Kcv36P36x4Sfr8XtMpANQBh8WqMd3O1E85v5qOAqAevHjWixoSP8R0DLdUp1W7qpXeOAWuoQS8xrudz6VMAl6MEcoTq/Z/me3bt5/yMZzhDcBX7YpN1gtFHMUIeDObxWY6gtuqVqH86quv1KNHD7344ouVbv/riCQjlLXgF2Q6AQAX2S023despUrsJaajAKhHjFCeWLVWeW/evFlBQUF6+eWX5e/vr7POOkuSdPXVV1c85qyzzlJhYaEk6bXXXpMkvfTSS2rSpEnFY1566SU98cQTFSftQFJAmOkEAFz0RtdztSF3k+kYAOoZI5QnVu1FOWVlZXr11Vf18MMPKz09Xf7+/hXHKjocDjmdTpWVlamsrEy33HKLHn744ePfzGLRkSNHFBUVVecfxGNt/kT64BrTKQDU0ramHTQqtFRljjLTUQDUs1nnzVLvpr1Nx3BL1T560d/fX+PHj9cVV1yh8ePHa86cOerbt6+ef/55NWt2/J5Mo0aNUpMmTRQUFCSHw6HS0lJlZGSocePGdZnf8wWEmk4AoJbKrP66LzZaZXlsEwT4An+bv+kIbqvGFwNERkbqvffe04cffqgvvvhC3bt31/fff3/c49q3b6+YmBiFh4crIiJCMTExat++fcU54PgNU96Ax3ql67naSpkEfEZ4QLjpCG6r1u3uH//4h3788Ud169ZNbdq0qctMvsU/xHQCALWwqUUXvZ67xXQMAA0oIiDCdAS3Ve0p76q0bdtWX3zxRV1l8U1MeQMep9QWqIlR4SrPzzEdBUADahTYyHQEt1XtEcq0tDQtWLCgPrP4Jqa8AY/zYteztTN/n+kYABpQsF+w/K1cQ3ki1S6Ur7/+uv7+979rxIgR2rx5syTpwQcfVLNmzdSqVSslJSWd9Ff37t31/vvv19sH8ViMUAIeZV18D72dwxZBgK+JCGS6+2SqXShHjx6tW265Rd988426deum1NRUFRcXKzAwUH379lVKSopSU1PVuXNn9erVS6mpqWrbtq26d++uNm3aaOPGjbr77rvr87N4Jv8QSWwID3iCooAQ3dcoQA6OTAV8TqMAprtPptrXULZu3VrPPfecJkyYoDfffFOtW7eWJA0aNEhvv/22Nm7cqG7duum1115TbGysrFarXnjhBbVt21aSdOaZZ+q7777Tnj171KpVq3r5MB7JapX8g6WyQtNJAJzCs53O0t7sX0zHAGAAI5QnV60RyoyMDOXm5qqwsFAxMTG68847VVhYqLKyPzby/f3YxRMdv3j99ddr1apVlMmqMO0NuL1ViX00J3uD6RgADGGE8uSqNUJ59tln65dfqv5X+ZVXXlnp+xMdvPPXx+FPAkKlgiOmUwA4gYLAcE0KdspZVK2DxQB4IQrlyVVrhHL8+PF6/PHH9dxzz+mFF16o+HX22WfXaZiZM2eqU6dOCgoKUvv27TVjxoyK+0aPHi2LxSKLxaKgoCAlJSVpzJgx2rKl8j5w3377rSwWiyIiIlRQUFDpviFDhshisejbb7+t9Jq/f29McKTZ9wdwUk92GqQDRemmYwAwiCnvk6vWCOX1119f8bXdbteBAwcUHx+vffv2af/+/ZUe++cp7xNNf1fl3nvv1ZQpU9ShQwfddNNNWr58ucaNG6ejR4/qvvvuq3jcVVddpdjYWP3000+aNWuWZs+erQ8++EB/+9vfKr1ebm6u3nvvPd1www3VzmBMaKzpBABO4Ls2p2t+FlPdgK9jhPLkarSx+cqVK3XDDTfIarVq7dq1kqQVK1ZozJgxys7OliTdeuutCg4OliTdc889ioj4o9FbLBa9/vrrx73umjVrNGXKFPXo0UPLly9XcHCwysrKNHjwYD344IO67rrrKh47duxYDRkyRJI0d+5cXXHFFRo9erS2b9+umJiYSq87ffp0zyiUYTGnfgyABpcT3FgPBhRJxaaTADCNQnly1d426LnnntMZZ5yhtLQ0XXrppSorK1NYWJhKSkr0/fffa9OmTWrbtq1+/vlnrVixQu3atdPmzZu1YsUK/fDDD1q2bJmWLFlS5Wu/+OKLkqTJkydXlFF/f3+NHTtW5eXl+vTTT6t83siRIzVq1ChlZ2fr3XffPe7+devWadWqVdX9iOaExZlOAKAKUzqcrvTiDNMxALgBprxPrtojlD169NDf/vY3TZs2TU2bNpUkTZw4URMnTnQ5xJIlS2Sz2TR06NBKt6ekpEiStm7desLnXnrppZo9e7a+/PJLTZgwoeL29u3ba+vWrZo+fbr69evncsZ6xZQ34Ha+anuGPsvaaDoGADfBCOXJVXuEcvDgwfrwww9VWlqq/Pz8Kh+zbt26itHG39nt9pO+7tGjR5Wenq6WLVsqNLTy9jldunTRO++8o1GjRp3w+V26dJGk4xbn9OvXTz179tTcuXOVmZl50gzGhVEoAXeSGRqth62c0w3gD4xQnly1C6UkFRQUKDExseI6yG3btumyyy6rWE29bNky3XnnnZKOFclbb71VycnJOnDgwAlf8/dFPbGxx5eqyMhIXXXVVerfv/8Jnx8Xd2y6OCPj+Gmp8ePHq7i4WLNmzarmJzSEKW/ArTzSro8yS7JNxwDgRhihPLkaFcqgoCA5nU4FBgZKkvLy8vTRRx/Jaj32MoGBgRX32Ww2DRs2TIcOHdKFF16ovLy8Kl/z99HOoKCgWn2A36+5LCw8/qSZUaNGKTIyUjNmzDjh/phuIbyp6QQAfvNZ+zP1ZRZndQOorElwE9MR3Fq1rqFcs2aNHnjgAb355puSjpVF6ViZczqdeuqppyRJa9euVXl5uR5++OGKAtezZ0+tXLlS5513nj777DNFRlbec/H3MnqqqfETKS4+tvwyJCTkuPuCg4M1ZswYTZ06VV999VWtXr9BNGphOgEASUcaNdVjTvabBFBZdHC0QvyP7xn4Q7UK5axZs7R48WJ16tRJFoulogT+XiynTJki6VgpLC8v12OPPVbxXKfTKafTqZUrV+r222/XG2+8Uem1f99WKDc397j3Xb9+vfr161dpH8y/OnLk2Akz0dHRVd4/btw4PfPMM5o+fXp1PqoZ/kFScJRU5ObXegJe7oE23ZSbveXUDwTgU+LD401HcHvVmvJ+6aWXtGjRIrVq1UpOp1MzZszQtm3bVFJSIunYtZVFRUWaNm2aIiMjVVRUVPGruLhY+/bt0/vvv39cmZSkpKQk2Ww2paamyuFwVLovIyNDJSUlCgsLO2G2TZuOTU117ty5yvvbtGmj888/X59++ulxm7C7FUYpAaPmdxyq7ymTAKpAoTy1al9Def7552v16tWaOXOmtm/frh49emj27Nm6/fbbVVZWJkkqKSmpmIL+s+bNm2vkyJFVvm5QUJB69+6t/Px8rV69utJ969evlyR17NjxhLnmzZsnSRo+fPgJHzN+/HjZ7Xbt2LHj5B/SpEbNTScAfNbByHg9VX7ixYMAfFvL8JamI7i9Gi3KkY6dVPPTTz8pISFBTZs21VNPPVWxEGfEiBH673//W+MQN910kyRp0qRJFddSZmVlafr06QoICNCwYcOqfN4nn3yit99+Wy1atNCVV155wtcfNmyYEhMTa5yrQVEoASOcsuj+1u2UX1ZgOgoAN8UI5anV6OjF3yUnJ+vHH39Uo0aVl9C3bt1arVu3rvHrXXvttVqwYIHmz5+vfv36aeDAgVqwYIF27dqlKVOmVDpS8fXXX9fChQu1du1aLV26VFFRUZo3b57Cw8NP+PpWq1Xjxo3TXXfdVeX9zzzzjObMmXPc7U2aNNGjjz5a489TK435wwqYMKfzuVrFVDeAk6BQnlqtCqWk48qkKywWi+bOnaupU6dq5syZmj59upKSkvTaa69p7NixlR777rvvKjAwUAkJCbrtttt01113qXnzU4/ujRkzRpMmTapySn7BggVVPqdVq1YNVyibJDfM+wCosDc6Uc+WpJqOAcDNUShPzeJ06w0afUj6FunlE2/gDqBuOSxWje42ROty3PjaagDGhfqHauUVK03HcHs1voYS9SSqjWSxmU4B+Iy3u5xLmQRwSoxOVg+F0l34BUiRrU2nAHzCrtgUTSvcaToGAA9AoaweCqU7iW5rOgHg9cqtfprYrLlK7CWmowDwAGwZVD0USncSnWI6AeD1Xu9yrjbm7jYdA4CHYISyeiiU7oQRSqBebWvaUTPyt5qOAcCDUCirh0LpTiiUQL0pswXo3pgolTvKTUcB4EEolNVDoXQnTHkD9WZ6l3O0PX+v6RgAPIif1U9NQ5qajuERKJTuJCRKCo059eMA1MiGll31Ru5m0zEAeJiWYS1ls7KlX3VQKN0N095AnSrxC9LExqGyO+2mowDwMB2adDAdwWNQKN0N095AnXqhy9naXbDfdAwAHqhTk06mI3gMCqW7iW5nOgHgNX5K6KV3czaajgHAQ3WO7mw6gsegULqbZt1MJwC8QmFAqO4Lt8nhdJiOAsAD2Sw2dYhiyru6KJTupnl3zvQG6sAznYdoX+Eh0zEAeKjEiESF+IeYjuExKJTuJiBUiuVfRIArViT21QdZTHUDqD2un6wZCqU7atHTdALAY+UHNdKkYLuccpqOAsCDcf1kzVAo3VGL3qYTAB7riY5n6FDREdMxAHg4CmXNUCjdUYtephMAHum7Nqfr46wNpmMA8HD+Vn+1i2TXlZqgULqj2A6Sf6jpFIBHyQmJ1OSAQtMxAHiBlMgU+dv8TcfwKBRKd2S1HVvtDaDaHm1/mo4UZ5qOAcALdG7CdHdNUSjdFdPeQLV90W6QFrOqG0Ad4frJmqNQuisKJVAtGWExekRZpmMA8CKdotkyqKYolO6qJSu9gep4uG0vZZXmmI4BwEsE+wWrTUQb0zE8DoXSXUW0lMKamk4BuLUFHc7S11mbTccA4EXaR7WXzcqJdTVFoXRnjFICJ3Q4ormm2DlaEUDd4oSc2qFQurNWp5tOALitB5I6K68s33QMAF6mT9M+piN4JAqlO0s603QCwC3N63S2lmdvNR0DgJfxs/qpX7N+pmN4JAqlO4vrKIU3M50CcCsHIhP0dOk+0zEAeKHuMd0VysEitUKhdHdJQ0wnANyGUxbd36qtCso5EQdA3RvQYoDpCB6LQunumPYGKrzX5Vz9mLPddAwAXur05qxdqC0Kpbtrc6Yki+kUgHF7opP0fNFu0zEAeKmooCh1iOpgOobHolC6u7BYKY4tDODbHBar7mvRSkX2YtNRAHip05qfJouFAZzaolB6gjZMe8O3vdnlPP2cu9N0DABebEBzrp90BYXSE3AdJXzYjrh2eqngV9MxAHgxiyw6rflppmN4NAqlJ2h1uuQXZDoF0ODKrX6aGBenUkep6SgAvFi7qHaKDo42HcOjUSg9gX+wlNDfdAqgwc3sep4256WajgHAy7G623UUSk/BtDd8zJZmHfVq3hbTMQD4AK6fdB2F0lO0Oct0AqDBlNkCdG90pMod5aajAPBywX7B6hHbw3QMj0eh9BTNukoR8aZTAA3ipa7naEd+mukYAHxA36Z95W/zNx3D41EoPUmHi0wnAOrdLy276c2czaZjAPARHLdYNyiUnqTT300nAOpVsX+wJjYOkd1pNx0FgI/g+sm6QaH0JC37SI1amE4B1JvnOw9VasF+0zEA+IiUyBQlNEowHcMrUCg9icXCtDe81upWvTU7e4PpGAB8yPDE4aYjeA0Kpadh2hteqDAwTPeHWeSU03QUAD7CIguFsg5RKD1NfD8pvLnpFECderrTYO0vPGw6BgAf0j22u5qH8f+ndYVC6WksFqnDCNMpgDrzQ1J/fZjFVDeAhsXoZN2iUHoipr3hJfKCIjQpiHO6ATQsP4ufzmt9nukYXoVC6Yni+0thTU2nAFz2eMeBOlx01HQMAD6mf/P+igyKNB3Dq1AoPZHVyrQ3PN7SlIH6lKluAAYw3V33KJSeimlveLDskCg9aMszHQOADwr2C9bQhKGmY3gdCqWnSjhdCosznQKolUfa91NGSZbpGAB80OCWgxXiH2I6htehUHoqq1XqOtJ0CqDGlrQbrM+zNpmOAcBHMd1dPyiUnqzntaYTADVyNCxWjyrTdAwAPioiMEIDWw40HcMrUSg9WXSy1IpD7eE5HkzpqezSHNMxAPioc1qdI3+rv+kYXolC6ekYpYSH+KTDUH2bvdl0DAA+jOnu+kOh9HQd/yYFRZhOAZzUocYt9IT9oOkYAHxYXEicesf1Nh3Da1EoPZ1/EItz4PYeSOyovLJ80zEA+LDhScNlsVhMx/BaFEpvwLQ33NgHnc7RD9nbTMcA4MOsFqsua3uZ6RhejULpDZp2lpr3MJ0COM6+qARNLU0zHQOAjxvUcpBahrc0HcOrUSi9BaOUcDNOWXR/QooKywtNRwHg467scKXpCF6PQuktuvxT8g81nQKoMLvLeVqT86vpGAB8XHLjZPVv1t90DK9HofQWgeFS54tNpwAkSakxbfR80U7TMQBAo9qPMh3BJ1AovQnT3nADdotNE5snqNheYjoKAB/XKKCRRrQZYTqGT6BQepP4vlLTLqZTwMfN6nKufslldBKAeZekXKJgv2DTMXwChdLbnH6r6QTwYb/GtdPLBdtNxwAA2Sw2prsbEIXS23S6RIqIN50CPqjM6q+JcXEqc5SZjgIAGtxysJqHNTcdw2dQKL2NzU/qP850CvigmV3P1Za8VNMxAEASWwU1NAqlN+p5Led7o0Ftbt5JM3O3mI4BAJKklMgU9W3W13QMn0Kh9EaBYVLvMaZTwEeU2gI1sUljlTvLTUcBAElsFWQChdJb9btJsgWYTgEfMK3r2dqRz/GKANxDRGCELky60HQMn0Oh9FbhTaWul5lOAS/3c3x3vZWzyXQMAKhwSTJbBZlAofRmp98qyWI6BbxUUUCI7osIksPpMB0FACQd2yro8vaXm47hkyiU3iymndT2PNMp4KWe73SW9hQcMB0DACpckHQBWwUZQqH0dmx0jnqwunUfvZe9wXQMAKjgZ/HTTd1uMh3DZ1EovV3rAVKLXqZTwIsUBIbr/lDJKafpKABQ4W/Jf1N8OAd7mEKh9AUD/s90AniRpzoN0v7Cw6ZjAEAFf6u/bux6o+kYPo1C6Qs6jJCadTedAl7gf21O00dZTHUDcC//SPmHmoU1Mx3Dp1EofYHFIg2933QKeLjc4Ag9EFBiOgYAVBJkC9K/uv7LdAyfR6H0FclnS60Gmk4BD/Z4h4FKLz5qOgYAVHJZu8sUExJjOobPo1D6EkYpUUtfp5yhBUx1A3AzwX7BGttlrOkYEIXStyT0l1LYlxI1kxXaRA/Zck3HAIDjXNH+CkUFRZmOAVEofc/Q+8XpOaiJh9v1VWZJlukYAFBJmH+Yrut8nekY+A2F0tc07SJ1uth0CniIxe2H6MsszuoG4H6u7ni1IgIjTMfAbyiUvuis+ySrn+kUcHNHw+P0qJNFOADcT6OARrq649WmY+BPKJS+qEkbqfsVplPAzU1O7q6cUq6dBOB+RncarfCAcNMx8CcUSl81+B7JFmg6BdzUfzsO1bLsLaZjAMBxIgMjdWWHK03HwF9QKH1VRAupD1st4HiHGrfUk+UHTMcAgCqN7TJWIf4hpmPgLyiUvuyMOyQuaMafOGXRpMQOyi8rMB0FAI6TEJ6gUe1HmY6BKlAofVloE2nI3aZTwI180PkcrcjeZjoGAFTpzj53KsAWYDoGqkCh9HV9/yVFtzOdAm4grUkrTS3ZYzoGAFRpYIuBGhI/xHQMnACF0tfZ/KXzp5hOAcMcFqvui2+jovIi01EA4Dh+Vj/d3YcZNXdGoYSUPFRqN9x0Chj0TudztTZnh+kYAFClqzpcpdYRrU3HwElYnE6n03QIuIHMXdJL/SV7iekkaGC7YpN1WSOLSvjfHoAbig6O1sKLFyrUP9R0FJwEI5Q4JipJOv0W0ynQwOwWm+5r1pIyCcBtTeg1gTLpASiU+MOgO6TGCaZToAG90fU8bcjdZToGAFSpZ2xPjUgaYToGqoFCiT/4B0vDnjKdAg1kW9MOmp7PFkEA3JOf1U/39b9PFovFdBRUA4USlbU7nwU6PqDM6q+JsdEqc5SZjgIAVbq649VKiUwxHQPVRKHE8YY9IXGslVeb0fVcbctjz0kA7ql5aHON6zbOdAzUAIUSx2uccOx6SnilTS266I3cLaZjAMAJ3dvvXgX7BZuOgRqgUKJqp98qNe1iOgXqWIlfkCZGhavcWW46CgBU6az4szQ4frDpGKghCiWqZvOX/j5DsvqbToI6NK3L2dqZv890DACoUohfiP7T7z+mY6AWKJQ4saadpcEcdeUt1sX30Ns5G03HAIATuq3nbWoa2tR0DNQChRInN3CC1LyH6RRwUVFAiO5rFCCH02E6CgBUaUDzARrVfpTpGKglCiVOzuZ3bOrbFmg6CVzwbKeztLfwoOkYAFClxoGN9fCAh9lz0oNRKHFqse2lM7mmxVOtSuyjOdkbTMcAgBOadNokxYTEmI4BF1AoUT2n3yq17GM6BWqoIDBck4KdcsppOgoAVOmiNhfpnFbnmI4BF1EoUT1W27Gpb/YF8yhPdhqkA0XppmMAQJVahLXQf/oyA+YNKJSovuhkaej9plOgmr5rc7rmZzHVDcA9WS1WPTrwUYUFhJmOgjpAoUTN9BsnJZxuOgVOISe4sR4MKDIdAwBOaHSn0eoV18t0DNQRCiVqxmqV/v6S5B9qOglOYkqH05VenGE6BgBUqX1Ue93c/WbTMVCHKJSouagk6fwpplPgBL5qe4Y+y2IDcwDuKdAWqCkDp8jfxkls3oRCidrpda3UdaTpFPiLzNBoPWzNMR0DAE7otp63KTky2XQM1DEKJWrvwmel6HamU+BPHmnXR5kl2aZjAECV+jfrr6s6XGU6BuoBhRK1FxAqXfaW5B9iOgkkfdb+TH2Ztcl0DACoUqOARnpkwCOchuOlKJRwTWwH6YKpplP4vCONmuoxJ/tNAnBf9/e/X3GhcaZjoJ5QKOG67ldI3ZnCMOmBNt2UW5pnOgYAVOnydpfr/MTzTcdAPaJQom5c8LQU28l0Cp80v+NQfZ+9xXQMAKhS77jeurvv3aZjoJ5RKFE3/IOPXU/JiQcN6mBkvJ4qP2A6BgBUqWloU00dMlV+Vj/TUVDPKJSoO9Ep0ojnTafwGU5ZdH/rdsovKzAdBQCOE2QL0vNnPq+ooCjTUdAAKJSoW13+KfW6znQKnzCn87lalb3ddAwAqNKk0yapY5OOpmOggVAoUfeGPSE17Wo6hVfbG52oZ0tSTccAgCpd3fFqjWgzwnQMNCAKJeqeX6B0+WwpNNZ0Eq/ksFh1X8tEFZUXmY4CAMfp16yfbu91u+kYaGAUStSPxgnS5e9JfkGmk3idt7ucq3U5O0zHAIDjtAhroacHPS2b1WY6ChoYhRL1J76P9PeXJXEqQl3ZFZuiaYU7TccAgOME+wXr+TOfV+OgxqajwAAKJepX539IQ/5jOoVXKLf6aWKz5iqxl5iOAgDHeej0h9Quqp3pGDCEQon6N+RuqculplN4vNe7nKuNubtNxwCA41zX+TpOwvFxFqfT6TQdAj6gvER6a4SUtsp0Eo+0rWlHXR5arHJHuekoAFDJgOYD9PLZL8tqYYzKl/G/PhqGX+CxRTqNW5lO4nHKbAG6NyaKMgnA7cSHx+uJQU9QJkGhRAMKjZaumCsFNjKdxKNM73KOtufvNR0DACppEtREr5z9iiICI0xHgRugUKJhxXaQLp0lWdhSojo2tOyqN3I3m44BAJWE+4frlXNeUXyjeNNR4CYolGh4yWcfO00HJ1XiF6SJjUNld9pNRwGACkG2IL049EVWdKMSCiXM6HuDdPqtplO4tRe6nK3dBftNxwCACn4WPz09+Gn1iutlOgrcDIUS5pz7sNRrtOkUbumnhF56N2ej6RgAUMEiix4a8JAGxw82HQVuiEIJsy54Vur8T9Mp3EphQKjuC7fJ4XSYjgIAFe7qc5dGtBlhOgbcFIUSZlmt0sWvSO2Gm07iNp7pNET7Cg+ZjgEAFW7ocoOu6niV6RhwYxRKmGfzky59U0pkGmVFYl99kM1UNwD3cVnby3RrT655x8lRKOEe/AKlUe9LLfuYTmJMflAjTQq2yykOrwLgHs5vfb4m9p9oOgY8AIUS7iMgVLryQymui+kkRjzR8QwdKjpiOgYASDp2pOJjZzzGKTioFs7yhvvJPyLNOl/K2GE6SYNZljxAN9vTTMcAAElS15iumnnOTIX4h5iOAg/BPzvgfsJipGs+kSISTCdpEDkhkZrsV2A6BgBIkpIbJ+vloS9TJlEjFEq4p4iW0jUfS2FxppPUu0fbn6ajJZmmYwCAkhsna+a5MzmfGzVGoYT7atJGGv2ZFN7cdJJ680W7QVqcxapuAOZ1atJJs86bpejgaNNR4IEolHBv0SnSmMVSZGvTSepcRliMHlGW6RgAoF5xvfT6ea+rcVBj01HgoSiUcH+RraXrlkjR7UwnqVMPte2lrNIc0zEA+LgBzQdoxtkzFOofajoKPBiFEp6hUTPpusVS066mk9SJBR3O0jdZm03HAODjzk44Wy+e9aKC/IJMR4GHo1DCc4Q2kUYvlOL7mU7iksMRzTXFztGKAMy6qM1Fenrw0/K3+ZuOAi9AoYRnCYqQrv6vRx/T+EBSZ+WV5ZuOAcCHjWw3Uo8MeEQ2q810FHgJCiU8z+8n6rQdZjpJjc3rdI6WZ281HQOADxvTeYzu63+fLBaL6SjwIpyUA89lL5f++y9p40emk1TLgcgEXRIdooLyQtNRAPioW3vcqhu63mA6BrwQI5TwXDY/6ZLXpB5Xm05ySk5ZdH+rtpRJAEZYZNE9fe+hTKLeUCjh2axW6aIXpYETTCc5qfe6nKsfc7abjgHAB1ktVj14+oO6ssOVpqPAizHlDe+x9h1p4QTJUWY6SSV7opN0aWM/FdmLTUcB4GMCbYF6dOCjOq/1eaajwMsxQgnv0fNq6er5khud9OCwWHVfi1aUSQANLjY4Vm+e/yZlEg2CQgnvkjhIuv4rKSrJdBJJ0ptdztPPuTtNxwDgYzo16aT3L3xfnaM7m44CH8GUN7xTYaY05wpp7wpjEXbEtdPIMLtKHaXGMgDwPcNaD9NDAx7i9Bs0KEYo4Z1CoqRrPpW6jjTy9uVWP02Mi6NMAmgwFll0S49b9OTgJymTaHB+pgMA9cYvQLrkValJsrT0MUkNNxg/s+t52pyzocHeD4BvC/YL1pQzpmhowlDTUeCjmPKGb9gwT/r435K9pN7fakuzjroipFjljvJ6fy8AaB7aXC+c9YLaRbUzHQU+jClv+IYu/5RGL5RCouv1bcpsAbo3OpIyCaBB9IztqfcvfJ8yCeMolPAd8X2lG5dJLfvW21u81PUc7chPq7fXB4DfXZx8sV479zVFBUWZjgIw5Q0fZC+TvnxAWvlSnb7sLy276ZqAXNmd9jp9XQD4M5vFpv/X6//pmk7XmI4CVKBQwndt/lT65GapJMfllyr2D9al7bortWB/HQQDgKqF+4frycFPamCLgaajAJUw5Q3f1fEi6cZvpaZdXH6p5zsPpUwCqFcdm3TU+xe+T5mEW2KEEigrlpbcLf30Zq2evrpVb421HpGzAbclAuA7LLJodKfRuqXnLfK3+puOA1SJQgn8bv1caeEEqayg2k8pDAzTJckdtL/wcD0GA+CrYoNj9egZj6p/s/6mowAnxZQ38LtuI6UbvpGiq7/9xtOdBlMmAdSLIfFDNO+ieZRJeARGKIG/Ki2QFtwmbfjwpA/7Iam/bnQeaKBQAHxFkC1It/e+XZe3v9x0FKDaKJTAiax7V1p8j1Sad9xdeUERujipjQ4XHTUQDIC3SolM0ZNnPKnkyGTTUYAaYcobOJEeV0njlkutBhx31+MdB1ImAdSpKztcqTkXzKFMwiMxQgmcisMhrZgmffOIZC/R0pSBurV8r+lUALxEVFCUHh7wsAa1HGQ6ClBrFEqgug5vVvaSO/X3wDxllGSZTgPACwxoPkCPDHxE0cHRpqMALqFQAjVQ5ijTzF9mauaGmSp3lJuOA8BDBVgDdGvPW3VNx2tksVhMxwFcRqEEamFb5jbdv/x+bcncYjoKAA/Tt2lf3d//frWOaG06ClBnKJRALZU7yvX6htf1yi+vqMxRZjoOADcXFRSlO3rfoRFtRpiOAtQ5CiXgol+zftX9y+/XpoxNpqMAcEMWWXRJyiWa0GuCIgIjTMcB6gWFEqgDdoddb21+SzPWz1BReZHpOADcRHLjZE06bZJ6xPYwHQWoVxRKoA4dKjikZ356Rot3LzYdBYBBwX7BurHrjbqm0zXyt/qbjgPUOwolUA/WHl6rx398nEU7gA86o8UZmth/olqEtTAdBWgwFEqgnjicDs3/db5eXPeiMoszTccBUM9ig2N1d9+7dW7rc01HARochRKoZ7mluZr+83TN2TpH5U72rgS8jdVi1eXtLtctPW5RWECY6TiAERRKoIHszN6pJ358QisOrjAdBUAd6dyks+7rf586RXcyHQUwikIJNLCv936tp1c/rX35+0xHAVBLLcNa6raet+m81udx0g0gCiVgRKm9VG9tekszN8xkmyHAg0QGRurGbjfqsnaXsXob+BMKJWDQ4YLDenbts/ps12emowA4iSBbkK7ueLXGdB7DdZJAFSiUgBvYnLFZM9bP0NK0paajAPgTP4ufLkq+SP/u9m/FhcaZjgO4LQol4Ea2Zm7VjPUz9M3eb+QUfzUBU6wWq85vfb7+3f3fatWolek4gNujUAJuaFvmNr3yyyv6as9XFEuggZ2dcLbGdx+v5Mhk01EAj0GhBNzYr1m/6pVfXtGXe76Uw+kwHQfwame0OEM397hZHZt0NB0F8DgUSsAD7MzeqVd+eUWfp35OsQTqkEUWDWgxQDd2vVHdY7ubjgN4LAol4EF25ezSq7+8qiW7l8jutJuOA3isQFugLky6UNd0vEZJjZNMxwE8HoUS8ECpOal69ZdXtWj3IoolUANRQVG6vN3lGtl+pKKCokzHAbwGhRLwYHtz9+rtzW9r4a6FKigrMB0HcFvJjZN1dcerdWHShQqwBZiOA3gdCiXgBQrKCrRg5wLN3TZXO7J3mI4DuI3Tmp2mazpdo4EtBpqOAng1CiXgZdYcWqO52+bqq71fqdxRbjoO0OD8rf4anjhc13S6Rm0j25qOA/gECiXgpY4WHdVH2z/SvF/n6VDBIdNxgHoXGRipS9tdqlHtRyk6ONp0HMCnUCgBL2d32LVs3zLN3TZXKw6sYKN0eBWLLOoR20N/S/6bhicOV5BfkOlIgE+iUAI+ZG/uXs3dNlcf7/hYuaW5puMAtRYfHq8RSSN0YZsLFR8ebzoO4POspgMAaDgJjRJ0Z5879fWlX+uh0x9S1+iupiMB1dYooJEubXup3hn2jhZdskjjuo/ziDI5c+ZMderUSUFBQWrfvr1mzJhRcd/o0aNlsVhksVgUFBSkpKQkjRkzRlu2bKn0Glu3bpXNZtNpp51W6faysjI1b95cERERys7OrvSa3377bX1/NKAChRLwQUF+Qbo45WLNvmC2lvxjiSb0mqAOUR1MxwKO42fx05CWQzR18FQtvWypJp02yaNOtLn33nv1r3/9S06nUzfddJPCw8M1btw4PfLII5Ued9VVV2n8+PFKSEjQrFmz1L17d33yyScV97dv316XXHKJVq5cqR9//LHi9g8++EAHDx7U+PHj1bhx44b6WMBxmPIGUCEtN02f7/lcn6d+rq2ZW03HgQ/r2KSjLmpzkYYlDvPYDcjXrFmjPn36qEePHlq+fLmCg4NVVlamwYMHa/Xq1UpNTdXEiRP11ltvaenSpRoyZIgkae7cubriiivUqFEjbd++XTExMZKkdevWqWfPnrr88sv1/vvvS5L69u2rTZs2KTU1teJxo0ePPu41gfrGCCWACvGN4nV9l+v14YgPteDvC3Rz95uVEpliOhZ8RFxInMZ0HqOP//ax5l44V1d2uNJjy6Qkvfjii5KkyZMnKzg4WJLk7++vsWPHqry8XJ9++mmVzxs5cqRGjRql7OxsvfvuuxW39+jRQ8OHD9e8efO0f/9+rVixQqtXr9YNN9xQUSYBU/xMBwDgnlpHtNaN3W7Ujd1u1K6cXfo89XN9vvtz7czZaToavEi7yHYa1HKQBrUcpK4xXWW1eM84x5IlS2Sz2TR06NBKt6ekHPtH2tatJ54FuPTSSzV79mx9+eWXmjBhQsXtEydO1KJFizRt2jTt3r1bAQEBuvPOO+vnAwA1QKEEcEpJEUka122cxnUbpx1ZOyqmxXfn7DYdDR4m2C9Y/Zv116CWg3RGizMUFxpnOlK9OHr0qNLT09WqVSuFhoZWuq9Lly565513lJycXGmBzl8fI+m4xTmnn366zjzzTM2YMUP5+fkaM2aMWrRoUT8fAqgBCiWAGkmOTFZyZLLGdx+vXTm7tOrgKq06uEo/HvpReaV5puPBDSWEJ1QUyN5Ne/vEWdr79++XJMXGxh53X2RkpK666ipJOmGhjIs7VrQzMjKOu2/ixIk6++yzZbPZdPfdd9dVZMAlFEoAtZYUkaSkiCSNaj9KdoddWzK3aOXBlVp5cKV+Tv9ZJfYS0xFhgL/VXz3jempQi2NT2a0jWpuO1ODy8/MlSUFBtdto/fdrLgsLC4+7r1mzZhWPiYyMrGVCoG5RKAHUCZvVps7RndU5urOu73K9SuwlWpe+rmIEc3PGZtmddtMxUU9ahrVU32Z9NajFIPVv3l+h/qGnfpIXs1qPXQtqt9fuz3xxcbEkKSQk5Lj7Hn30UVmtVuXn5+v555/X5MmTa50TqCsUSgD1ItAWqP7N+qt/s/6SpNzSXK0+tFqrDq7SyoMruf7SgwX7BatzdGd1je6qbjHd1DWmq5oENzEdy61ERERIknJzjz+Rav369erXr5+uv/76Ez7/yJEjkqTo6Mpnku/YsUNz587V5Zdfrt27d+uFF17Q7bffrvDw8DpMD9QchRJAg2gU0EhDE4ZqaMKxFa/phelac2iNtmRu0ZbMLdqauVU5JTmGU6IqrRu1VteYP8pjSuMU2aw207HcWlJSkmw2m1JTU+VwOCpGLKVj10WWlJQoLCysYmr8rzZt2iRJ6ty5c6XbH3vsMdntdt1xxx3atWuX/vnPf2ratGn6z3/+U38fBqgGCiUAI2JDYjU8abiGJw2vuO1A/oGKcrk1Y6u2ZG7R4cLDBlP6njD/MHWO7lxRHrvFdFNEYITpWB4nKChIvXv31qpVq7R69Wr169ev4r7169dLkjp27KhDhw5V+fx58+ZJkoYP/+Pvx549e/Tuu+/qrLPOUo8ePdStWzclJSXp2Wef1W233Vbl9DjQUCiUANxG87Dmah7WvGIUU5IyizMryuXWzK3amrlVe3L3yCkO+XJFoC1QCY0S1LpR62O/IlqrY1RHJTVO8qq9IE266aabtGrVKk2aNEmLFi2SzWZTVlaWpk+froCAAA0bNkzffPPNcc/75JNP9Pbbb6tFixa68sorK25/4oknVFZWpjvuuEPSses0J0yYoFtuuUWvvPJKpf0qgYbG0YsAPE5hWaG2ZW3TlowtSstL08GCgzqQf0AHCg4wbf4nVotVTUOaqnVEa7Vq1KpSeWwW2kwWi8V0RK/mdDr1z3/+U/Pnz1evXr00cOBALViwQLt27dKUKVN0zz33VByTeNVVVykuLk5r167V0qVLFRUVpc8++0z9+x+7BvngwYNKTExUcnKyNm7cWPEeBQUFio+PV3BwsHbt2qXAwMCK1xwxYoSaN29+XK4mTZro0UcfbbD/DvANjFAC8Dgh/iHqEdtDPWJ7HHdfQVmBDuQf0MGCg9qfv18H8w/qQMGBY4Uz/4Ayio/f18+ThfiFKDIoUtHB0X+Uxt8KZKtGrRRoCzQd0WdZLBbNnTtXU6dO1cyZMzV9+nQlJSXptdde09ixYys99t1331VgYKASEhJ022236a677qpUBp966imVlJTo9ttvr/S80NBQ3XTTTZoyZYreeOMNjRs3ruK+BQsWVJmrVatWFErUOUYoAfiUEnvJscL5W9E8XHhYuSW5yi3NVV5pnnJLc5Vb8sfXxfbiBs0XHhCuqKAoRQZGKjIo8tjXQZHHff/77xRGAO6AQgkAJ1FqL1VhWaGKyouO/bIXqais6I/vy4tU7iiXzWqTzfLbr6q+PsH9VotV/lZ/hQeEq3FQY/lb/U1/ZACoMQolAAAAXMJSPgAAALiEQgkAAACXUCgBAADgEgolAAAAXEKhBAAAgEsolAAAAHAJhRIAAAAuoVACAADAJRRKAAAAuIRCCQAAAJdQKAEAAOASCiUAAABcQqEEAACASyiUAAAAcAmFEgAAAC6hUAIAAMAlFEoAAAC4hEIJAAAAl1AoAQAA4BIKJQAAAFxCoQQAAIBLKJQAAABwCYUSAAAALqFQAgAAwCUUSgAAALiEQgkAAACXUCgBAADgEgolAAAAXEKhBAAAgEsolAAAAHAJhRIAAAAuoVACAADAJRRKAAAAuIRCCQAAAJdQKAEAAOASCiUAAABcQqEEAACASyiUAAAAcAmFEgAAAC6hUAIAAMAlFEoAAAC4hEIJAAAAl1AoAQAA4BIKJQAAAFxCoQQAAIBLKJQAAABwCYUSAAAALqFQAgAAwCUUSgAAALiEQgkAAACXUCgBAADgEgolAAAAXEKhBAAAgEsolAAAAHAJhRIAAAAuoVACAADAJRRKAAAAuIRCCQAAAJdQKAEAAOASCiUAAABcQqEEAACASyiUAAAAcAmFEgAAAC6hUAIAAMAlFEoAAAC4hEIJAAAAl1AoAQAA4BIKJQAAAFxCoQQAAIBLKJQAAABwyf8HlupgJWteov4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (16.0, 8.0) # 设置figure_size尺寸\n",
    "plt.rcParams['font.sans-serif']=['SimHei']    # 用来设置字体样式以正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False    # 默认是使用Unicode负号，设置正常显示字符，如正常显示负号\n",
    "plt.rcParams['font.size'] = 15\n",
    "\n",
    "(data.groupby(\"门店编号\")[\"销售额\"].sum()/data[\"销售额\"].sum()).plot.pie()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 哪个时间段是超市的客流高封期?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='小时'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 利用自定义时间格式函数strftime提取小时数\n",
    "data[\"小时\"]=data[\"成交时间\"].map(lambda x:int(x.strftime(\"%H\")))\n",
    "# 对小时和订单去重\n",
    "traffic=data[[\"小时\",\"订单ID\"]].drop_duplicates()\n",
    "# 求每小时的客流量\n",
    "traffic.groupby(\"小时\")[\"订单ID\"].count().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
